ReviewAsian Pacific journal of cancer prevention : APJCP2018
Errors in Mammography Cannot be Solved Through Technology Alone
Review in Asian Pacific journal of cancer prevention : APJCP, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 97 citations in OpenAlex.
- MassSeg-Framework: A Breast Mass Detection and Segmentation Framework Based on Deep Learning and an Active Contour Model.Life (Basel, Switzerland) · 2026Article
- Article
- Why we still miss breast cancers: strategies for improving mammography interpretation.Insights into imaging · 2026Article
- Impact of Prior Mammograms on Radiologists and Radiographers' Detection of Different Breast Cancer Lesion Types.Journal of medical radiation sciences · 2025Article
- From lab to life: technological innovations in transforming cancer metastasis detection and therapy.Discover oncology · 2025Review
- Analyzing explainability of YOLO-based breast cancer detection using heat map visualizations.Quantitative imaging in medicine and surgery · 2025Article
- A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult.Journal of imaging informatics in medicine · 2025Article
- Controversies in the Application of AI in Radiology-Is There Medico-Legal Support? Aspects from Romanian Practice.Diagnostics (Basel, Switzerland) · 2025Review
- Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.Bioengineering (Basel, Switzerland) · 2024Review
- Review
- Article
- Article
- Artificial Intelligence Improves Detection of Supplemental Screening Ultrasound-detected Breast Cancers in Mammography.Journal of breast cancer · 2023Article
- Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study.Scientific reports · 2023Article
- Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases.Journal of digital imaging · 2023Article
- Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study.Journal of personalized medicine · 2023Article
- Signals of global symmetry are important for abnormality detection in mammograms.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Trends in research of exosomes associated with breast cancer over the past decade: a scientometric analysis.Frontiers in oncology · 2023Article
- Does the availability of prior mammograms improve radiologists' observer performance?-a scoping review.BJR open · 2023Article
- Breast cancer patient characterisation and visualisation using deep learning and fisher information networks.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mammography has been the frontline screening tool for breast cancer for decades. However, high error rates in the form of false negatives (FNs) and false positives (FPs) have persisted despite technological improvements. Radiologists still miss between 10% and 30% of cancers while 80% of woman recalled for additional views have normal outcomes, with 40% of biopsied lesions being benign. Research show that the majority of cancers missed is actually visible and looked at, but either go unnoticed or are deemed to be benign. Causal agents for these errors include human related characteristics resulting in contributory search, perception and decision-making behaviours. Technical, patient and lesion factors are also important relating to positioning, compression, patient size, breast density and presence of breast implants as well as the nature and subtype of the cancer itself, where features such as architectural distortion and triple-negative cancers remain challenging to detect on screening. A better understanding of these causal agents as well as the adoption of technological and educational interventions, which audits reader performance and provide immediate perceptual feedback, should help. This paper reviews the current status of our knowledge around error rates in mammography and explores the factors impacting it. It also presents potential solutions for maximizing diagnostic efficacy thus benefiting the millions of women who undergo this procedure each year.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.